Systematic identification of personal tumor-specific neoantigens in chronic lymphocytic leukemia

Abstract

Genome sequencing has revealed a large number of shared and personal somatic mutations across human cancers. In principle, any genetic alteration affecting a protein-coding region has the potential to generate mutated peptides that are presented by surface HLA class I proteins that might be recognized by cytotoxic T cells. To test this possibility, we implemented a streamlined approach for the prediction and validation of such neoantigens derived from individual tumors and presented by patient-specific HLA alleles. We applied our computational pipeline to 91 chronic lymphocytic leukemias (CLLs) that underwent whole-exome sequencing (WES). We predicted ∼22 mutated HLA-binding peptides per leukemia (derived from ∼16 missense mutations) and experimentally confirmed HLA binding for ∼55% of such peptides. Two CLL patients that achieved long-term remission following allogeneic hematopoietic stem cell transplantation were monitored for CD8(+) T-cell responses against predicted or confirmed HLA-binding peptides. Long-lived cytotoxic T-cell responses were detected against peptides generated from personal tumor mutations in ALMS1, C6ORF89, and FNDC3B presented on tumor cells. Finally, we applied our computational pipeline to WES data (N = 2488 samples) across 13 different cancer types and estimated dozens to thousands of predicted neoantigens per individual tumor, suggesting that neoantigens are frequent in most tumors.

Figure 1

Schematic representation of a strategy to systematically discover tumor neoantigens. Tumor-specific mutations in cancer samples are detected using WES or WGS and identified through the application of mutation calling algorithms (such as Mutect). Next, candidate neoepitopes can be predicted using well-validated algorithms (NetMHCpan), and their identification can be refined by experimental validation for peptide-HLA binding and by confirmation of gene expression at the RNA level. These candidate neoantigens can be subsequently tested for their ability to stimulate tumor-specific T-cell responses.

Figure 2

Frequency of classes of point mutations that have the potential to generate neoantigens in CLL. Analysis of WES and WGS data generated from 91 CLL cases reveals that (A) missense mutations are the most frequent class of the somatic alterations with the potential to generate neoepitopes, whereas (B) frameshifts and (C) splice-site mutations constitute rare events.

Figure 3

Application of the NetMHCpan prediction algorithm to CLL cases. (A) Distribution of the number of predicted peptides with HLA binding affinity <150 (black) and 150 to 500 nM (gray) across 31 CLL patients with available HLA typing information (supplemental Table 1). (B) Peptides with predicted binding (IC₅₀ <500 nM by NetMHCpan) from 4 patients were synthesized and tested for HLA-A and -B allele binding using a competitive MHC I allele-binding assay. The percent of predicted peptides with evidence of experimental binding (IC₅₀ < 500 nM) are indicated. (C) The distribution of gene expression for all somatically mutated genes (n = 347) from 26 CLL patients and for the subset of gene mutations encoding neoepitopes with predicted HLA binding scores of IC₅₀ <500 nM (n = 180). No-low, genes within the lowest quartile expression; medium, genes within the 2 middle quartiles of expression; high, genes within the highest quartile of expression.

Figure 4

Mutations in ALMS1 and C6ORF89 in patient 1 generate immunogenic peptides. (A) Twenty-five missense mutations were identified in patient 1 CLL cells, from which 30 putative epitopes (from 25 unique peptides; 5 peptides were predicted to bind to >1 HLA allele) from 13 mutations were predicted to bind to patient 1’s MHC class I alleles. A total of 13 peptides from 8 mutations were experimentally confirmed as HLA binding. Posttransplant T cells (7 years) from patient 1 were stimulated weekly ex vivo for 4 weeks with 5 pools of 6 mutated peptides per pool (supplemental Table 4), and subsequently tested by the IFN-γ ELISPOT assay. (B) Increased IFN-γ secretion by T cells was detected against pool 2 peptides. Negative control, irrelevant tax peptide; positive control, PHA. (C) Of pool 2 peptides, patient 1 T cells were reactive to mutated ALMS1 and C6ORF89 peptides (right, averaged results from duplicate wells are displayed). (Left) The predicted and experimental IC₅₀ scores (nM) of mutated and wild-type ALMS1 and C6ORF89 peptides.

Figure 5

Mutated FNDC3B generates a naturally immunogenic neoepitope in patient 2. (A) Twenty-six missense mutations were identified in patient 2 CLL cells, from which 37 epitopes (from 36 unique peptides; 1 peptide was predicted to bind to 2 HLA alleles) from 16 mutations were predicted to bind to patient 2’s MHC class I alleles. A total of 18 peptides from 12 mutations were experimentally confirmed to bind. Posttransplant T cells (∼4 years) from patient 2 were stimulated with autologous DCs or B cells pulsed with 3 pools of experimentally validated binding mutated peptides (18 peptides total) for 2 weeks ex vivo (supplemental Table 5). (B) Increased IFN-γ secretion was detected by ELISPOT assay in T cells stimulated with pool 1 peptides. (C) Of pool 1 peptides, increased IFN-γ secretion was detected against the mut-FNDC3B peptide (lower, averaged results from duplicate wells are displayed). (Upper) Predicted and experimental IC₅₀ scores of mut- and wt-FNDC3B peptides. (D) T cells reactive to mut-FNDC3B demonstrate specificity to the mutated epitope but not the corresponding wild-type peptide (concentrations: 0.1-10 µg/mL) and are polyfunctional, secreting IFN-γ, GM-CSF, and IL-2 (Tukey post-hoc tests from 2-way analysis of variance modeling; mut vs wt). (E) (Left) Mut-FNDC3B-specific T cells are reactive in a class I-restricted manner and (right) recognize an endogenously processed and presented form of mutated FNDC3B, because they recognized HLA-A2 APCs transfected with a plasmid encoding a minigene of 300 bp encompassing the FNDC3B mutation (2-sided Welch t test) but not wild-type sequences beyond the background of nontransfected APCs alone. (Top right) Western blot analysis confirming expression of minigenes encoding mut- and wt-FNDC3B. (F) Patient 2 CD8⁺ T cells specifically recognizing mut-FNDC3B (5 μg/mL) demonstrate antitumor responses in an IFN-γ ELISPOT assay (2-sided Welch t test). (G) Specificity of T cells recognizing the mut-FNDC3B epitope detected by HLA-A2⁺/mut⁻ FNDC3B tetramer-positive T cells in patient 2.

Figure 6

Kinetics of the mut-FNDC3B-specific T-cell response in relation to posttransplant course and CD107a degranulation in the presence of tumor cells. (A) (Top) Molecular tumor burden was measured in patient 2 using a patient tumor-specific Taqman PCR assay based on the clonotypic IgH sequence at serial time points before and after HSCT (supplemental Methods). (Middle) Detection of mut-FNDC3B-reactive T cells in comparison with wt-FNDC3B or irrelevant peptides from peripheral blood before and after allo-HSCT by IFN-γ ELISPOT following stimulation with peptide-pulsed autologous B cells. The number of IFN-γ-secreting spots per cells at each time point was measured in triplicate (Welch t test; mut vs wt). (Bottom) Detection of mut-FNDC3B-specific TCR Vβ11 cells by nested clone-specific CDR3 PCR before and after HSCT in peripheral blood of patient 2 (supplemental Methods; supplemental Figure 3). Triangles, time points at which a sample was tested; black, amplification detected, where + indicates detectable amplification up to twofold and ++ indicates more than twofold greater amplification than the median level of all samples with detectable expression of the clone-specific Vβ11 sequence. (B) Evaluation of CD107a expression on 6m/32m mut-FNDC3B-reactive posttransplant CD8⁺ T cells (effectors) in the presence of patient 2 CLL cells or control donor-engrafted normal B cells in patient 2. The numbers indicate the percentage of mut-FNDC3B tetramer-positive cells that are also CD107a positive. For controls (tetramer negative T cells and nonrelated HLA-A*02:01 tumors), please see supplemental Figures 4 and 5.

Figure 7

Estimation of tumor neoantigen load across cancers. (A) Box plots comparing overall somatic mutation rates detected across cancers by massively parallel sequencing. AML, acute myeloid leukemia; DLBCL, diffused large B-cell lymphoma; ESO AD, esophageal adenocarcinoma; GBM, glioblastoma; LUAD, lung adenocarcinoma; LUSC, lung squamous cell carcinoma; clear cell RCC, clear cell renal carcinoma; papillary RCC, papillary renal cell carcinoma. The distributions (shown by box plot) of (B) the number of missense, frameshift, and splice-site mutations per case across 13 cancers, (C) the summed neoORF length generated per sample and of the predicted neopeptides with (D) IC₅₀ <150 and (E) <500 nM generated from missense and frameshift mutations. For all panels, the left and right ends of the boxes represent the 25th and 75th percentile values, respectively, and the segment in the middle is the median. The left and right extremes of the bars extend to the minimum and maximum values.